Shun Zhenming, Chi Eric, Durrleman Sylvain, Fisher Lloyd
Biometrics and Data Management, Aventis Pharma, 200 Crossing, Bridgewater, NJ 08807, USA.
Stat Med. 2005 Jun 15;24(11):1619-37; discussion 1639-56. doi: 10.1002/sim.2015.
As a regulatory strategy, it is nowadays not uncommon to conduct one confirmatory pivotal clinical trial, instead of two, to demonstrate efficacy and safety in drug development. This paper is intended to investigate the statistical foundation of such an approach. The one-study approach is compared with the conventional two-study approach in terms of power, type-I error, and fundamental statistical assumptions. Necessary requirements for a single-study model is provided in order to maintain equivalent evidence as that from a two-study model. In general, one-study model is valid only under a 'one population' assumption. In addition, higher data quality and more convincing and robust results need to be demonstrated in such cases. However, when 'one-population' assumption is valid and appropriate methods are selected, a one-study model can have a better power using the same sample size. The paper also investigates statistical assumptions and methods for making an overall inference when a two-study model has been used. The methods for integrated analysis are evaluated. It is important for statisticians to select correct pooling strategy based on the project objective and statistical hypothesis.
作为一种监管策略，如今在药物研发中进行一项确证性关键临床试验而非两项来证明疗效和安全性的情况并不少见。本文旨在探究这种方法的统计学基础。将单试验方法与传统的双试验方法在检验效能、I 型错误和基本统计假设方面进行比较。为了保持与双试验模型等效的证据，给出了单试验模型的必要要求。一般来说，单试验模型仅在“单一总体”假设下有效。此外，在这种情况下需要展示更高的数据质量以及更具说服力和稳健性的结果。然而，当“单一总体”假设有效且选择了合适的方法时，单试验模型在使用相同样本量的情况下可以具有更好的检验效能。本文还研究了在使用双试验模型时进行总体推断的统计假设和方法。对综合分析方法进行了评估。统计学家根据项目目标和统计假设选择正确的合并策略很重要。
